Getting insight into the internal structures and processes of trained DNN is crucial because such models work as black-boxes [1]}.
Consequently, researchers proposed various methods for visualizing and analyzing DNN.
These approaches are often best suitable for image data [2]}, [3]}, [4]} because input images and the corresponding model explanations are visually interpretable by a human.
Popular DNN explanation techniques aim to visualize the learned features of the model [1]}, [6]}, [7]}, highlighting prediction-relevant values in the input [6]}, [9]}, [2]} or analyzing the learned representations [11]}, [12]}, [13]}, [14]}.
